Food allergens, which are typically proteins or glyco-proteins of size 10-70 kDa, present a serious public health problem, in part because allergy immunotherapy is currently unavailable. Most food allergies are caused by crustaceans, fish, eggs, peanuts, milk, tree nuts, soybeans, or gluten-containing cereals. Food allergies are especially common among children, afflicting about 6-8% of children less than three years of age. (See S. A. Bock, “Prospective appraisal of complaints of adverse reactions to foods in children during the first three years of life,” Pediatrics 79, 683 (1987) hereby incorporated herein by reference.) Peanuts are considered one of the most dangerous food allergens, with severe anaphylactic reactions causing over 100 fatalities in the USA alone each year. (See W. Burks, G. A. Bannon, S. Sicherer and H. A. Sampson, “Peanut-induced anaphylactic reactions,” Inter. Arch. Allergy Immunol. 119, 165 (1999) hereby incorporated by reference.) Exposure to peanut allergens is often inadvertent, arising from ingestion of foods not believed to contain peanuts. (See T. J. Furlong, J. DeSimone and H. Sicherer, “Peanut and tree nut allergic reactions in restaurants and other food establishments,” J. Allergy Clin. Immunol. 108, 867 (2001) hereby incorporated by reference.) Nine allergens within peanuts have been identified, Ara h1 to Ara h 8, and peanut oleosin. (See G. W. Palmer, D. A. Dibbern, A. W. Burks, G. A. Bannon, S. A. Bock, H. S. Porterfield, R. A. McDermott and S. C. Dreskin, “Comparative potency of Ara h1 and Ara h 2 in immunochemical and functional assays of allergenicity,” Clin. Immunol. 115, 302 (2005) and 5. A. Barre, J. P. Borges, R. Culerrier, and P. Rouge, “Homology modeling of the major peanut allergen Ara h 2 and surface mapping of IgE-binding epitopes,” Immunol. Lett. 100, 153 (2005) both hereby incorporated by reference.) Ara h 1 and Ara h 2 are widely described as the most important allergens, although this has been disputed.
Current methods for detecting peanut proteins are based on enzyme linked immuno-sorbent assays (ELISA), (See A. Pomes, R. M. Helm, G. A. Bannon, A. W. Burks, A. Tsay and M. D. Chapman, “Monitoring peanut allergen in food products by measuring Ara h 1,” J. Allergy Clin. Immunol. 111, 640 (2003); M. L. Nogueira, R. McDonald and C. Westphal, “Can commercial peanut assay kits detect peanut allergens?,” J. AOAC Int. 87, 1480 (2004); D. A. Schmitt, H. Cheng, S. J. Maleki and A. W. Burks, “Competitive inhibition ELISA for quantification of Ara h 1 and Ara h 2, the major peanut allergens,” J. AOAC Int. 87, 1492 (2004) and M. Kiening, R. Niessner, E. Drs, S. Baumgartner, R. Krska, M. Bremer, V. Tomkies, P. Reece, C. Danks, U. Immer and M. G. Weller, “Sandwich immunoassays for the determination of peanut and hazelnut traces in foods,” J. Agric. Food Chem. 53, 3321 (2005) all of which are hereby incorporated herein by reference) which are time consuming, require trained personnel, are difficult to automate and miniaturize, and are not fully standardized. (See A. L. Ghindilis, P. Atanasov, M. Wilkins and E. Wilkins, “Immunosensors: Electrochemical sensing and other engineering approaches,” Biosens. Bioelectron. 13, 113 (1998) herby incorporated by reference. For these reasons, ELISA is unlikely to be practical for point-of-use applications, where portable and immediate detection is needed, so alternative immunosensors to ELISA are considered highly desirable. (See I. Mohammed, W. M. Mullett, E. P. C. Lai and J. J. Yeung, “Is biosensor a viable method for food allergen detection?” Anal. Chim. Acta 444, 97 (2001) hereby incorporated herein by reference.)
Biosensors for food allergens have been reported using capillary electrophoresis/laser-induced fluorescence, (See M. T. Veledo, M. de Frutos and J. C. Diez-Masa, “Analysis of trace amounts of bovine β-lactoglobulin in infant formulas by capillary electrophoresis with on-capillary derivatization and laser-induced fluorescence detection,” J. Separ. Sci. 28, 941 (2005) hereby incorporated herein by reference) liquid chromatography/mass spectrometry, (See K. J. Shefcheck and S. M. Musser, “Confirmation of the allergenic peanut protein, Ara h 1, in a model food matrix using liquid chromatography/tandem mass spectrometry (LC/MS/MS),” J. Agric. Food Chem. 52, 2785 (2004) hereby incorporated herein by reference) and electrochemical impedance spectroscopy (EIS). (See H. Huang, P. Ran and Z. Liu, “Impedance sensing of allergen-antibody interaction on glassy carbon electrode modified by gold electrodeposition,” Bioelectrochemistry 70, 257 (2007) hereby incorporated herein by reference.)
Electrochemical impedance spectroscopy involves application of a small sinusoidal AC voltage probe to an electrode and determination of the current response. Electrochemical impedance spectroscopy has been previously employed as a transduction method for biological recognition, and detection limits have been reported in the nM to pM range for impedance biosensors. However, the use of impedance biosensors for detecting food allergens has not been reported. One disadvantage of many biosensor methods, including electrochemical impedance spectroscopy, is that non-specific interactions can venerate false positives. In other words, species other than the desired analyte, such as peanut protein, can also interact with the surface-immobilized antibody.
This specificity shortcoming can be overcome by using a bioelectronic tongue, which is an array of antibody-coated electrodes whose response to real food matrices is analyzed with signal processing algorithms to separate the electronic signature of peanut protein from that of interfering species. The bioelectronic tongue can be distinguished from an electronic tongue by the incorporation of biomolecules, such as antibodies, as will be discussed in detail below. The response of each sensor to a specific sample is converted to a digital time-series for each sensor. The array of sensors results in a numerical matrix that must be analyzed to unearth the pattern. The pattern analysis protocol includes signal preprocessing to prepare the data, dimensionality reduction to extract relevant features from the data, and classification through automated classifiers that could include clustering, k-nearest neighbor classification, neural networks, and regression.